Unified Multimodal Large Language Models (U-MLLMs) integrate understanding and generation within a single architecture. However, existing evaluations typically assess these capabilities separately, overlooking semantic equivalence, i.e., the ability to manifest consistent reasoning results regardless of the output modality. In this work, we investigate whether current U-MLLMs satisfy this premise. We observe that while models demonstrate robust textual reasoning, they fail to maintain semantic equivalence when required to render the same results in the image modality. To rigorously diagnose this discrepancy, we introduce VGUBench, a framework to decouple reasoning logic from generation fidelity. VGUBench comprises three diagnostic tasks: (1)Textual Generative Understanding, establishing a baseline for reasoning accuracy in textual response; (2)Visual Generative Understanding, evaluating the ability to generate visual responses that represent the correct answer; and (3)a Visual Rendering control task, which assesses the ability to directly render explicit visual descriptions into images without complex reasoning. Our evaluation reveals a significant disparity: despite strong performance in textual understanding and visual rendering, U-MLLMs exhibit a marked performance collapse when required to generate visual answers to questions. Furthermore, we find a negligible correlation between visual answering performance and basic rendering quality. These results suggest that the failure stems not from insufficient generation fidelity, but from a breakdown in cross-modal semantic alignment. We provide diagnostic insights to address this challenge in future Unified Generation and Understanding Models.
COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Baidu Inc.（百度公司）
Towards Stable Cross-Domain Depression Recognition under Missing Modalities
School of Future Technology, South China University of Technology（未来技术学院，华南理工大学）；PengCheng Laboratory（鹏城实验室）；School of Software Engineering, South China University of Technology（软件工程学院，华南理工大学）；Artificial Intelligence Research Institute, Shenzhen MSU-BIT University（人工智能研究院，深圳MSU-BIT大学）；Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing（粤港澳大湾区情感智能与泛在计算联合实验室）；School of Computer Science and Technology, Guangdong University of Technology（计算机科学与技术学院，广东工业大学）；Department of Computer Science, City University of Hong Kong（计算机科学系，香港城市大学）
Generating Multimodal Driving Scenes via Next-Scene Prediction
School of Software Engineering, XJTU（西安交通大学软件学院）；Horizon Robotics（地平线机器人）；School of Computer and Communication Sciences, EPFL（洛桑联邦理工学院计算机与通信科学学院）；University of Chinese Academy of Sciences（中国科学院大学）
Contrastive Regularization over LoRA for Multimodal Biomedical Image Incremental Learning
School of Computer Science and Engineering, Central South University（计算机科学与工程学院，中南大学）；School of Automation, Central South University（自动化学院，中南大学）；School of Computer Science and Technology, Beijing Jiaotong University（计算机科学与技术学院，北京交通大学）
Asymmetric Reinforcing against Multi-modal Representation Bias
College of Intelligence and Computing, Tianjin University（天津大学智能与计算学部）；The State Key Laboratory of Integrated Services Networks, Xidian University（西安电子科技大学综合业务网理论及关键技术国家重点实验室）
Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）；University of Cordoba（科尔多瓦大学）；The Hong Kong Polytechnic University（香港理工大学）
Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities
Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China)（青岛软件研究所，计算机科学与技术学院，中国石油大学（华东））；Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software（山东省智能油气工业软件重点实验室）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
UNIV: Unified Foundation Model for Infrared and Visible Modalities
Research Center for Intelligent Computing Systems, CAS ICT（智能计算系统研究所以及中国科学院信息科技研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
AMGFormer: Adaptive Multi-Granular Transformer for Brain Tumor Segmentation with Missing Modalities
1 Faculty of Information Engineering；Automation, Kunming University of Science；Technology 2 Hepatobiliary \& Pancreatic Surgery, First People's Hospital of Yunnan/KUST Affiliated Hospital
Robust Multispectral Semantic Segmentation under Missing or Full Modalities via Structured Latent Projection
Department of Computer Engineering, Ankara University（安卡拉大学计算机工程系）；Department of Modeling and Simulation, Graduate School of Informatics, METU（信息学院建模与模拟系，METU）
Multi-Modal Image Fusion via Intervention-Stable Feature Learning
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；School of Artificial Intelligence, Nanyang Normal University（南阳师范学院人工智能学院）；Department of Electrical and Electronic Engineering, Hong Kong Polytechnic University（香港理工大学电子与电气工程系）
